# A Hierarchical Framework for Graph Structure Learning in Histopathology Image Classification Source: [A Hierarchical Framework for Graph Structure Learning in Histopathology Image Classification]() Collection index: [llms.txt]() Data updated: 2026-07-31T05:00:01.455Z Authors: Sudipta Paul, Amanda W. Lund, B\\"{u}lent Yener Published: 2026-07-28T18:00:52.000Z ## Abstract The spatial organization of cells and tissues provides important diagnostic cues in histopathology images. Although graph-based approaches can model these relationships, many rely on fixed or heuristic graph structures that may not accurately represent tissue connectivity. In this work, we propose $G\_2^\*$-Net, an optimized two-level graph learning framework for classifying large-scale histopathology images, such as whole-slide images (WSIs) or large regions of interest (ROIs). Here, $G\_2$ denotes the two-level hierarchical graph representation, and the superscript $\*$ indicates the optimized image-level graph structure learned from the proposed framework. The method first divides each WSI or large ROI into image patches, constructs cell-level graphs within each patch to capture local tissue architecture, and then represents each patch as a node in a learnable image-level graph. $G\_2^\*$-Net formulates image-level graph structure learning as a second-order bilevel optimization problem, separating graph connectivity learning from classifier optimization while coupling them through validation-driven feedback. To make this formulation computationally practical, we adopt a DARTS-inspired one-step unrolled approximation for efficient hypergradient estimation. Experimental validation on three distinct histopathology datasets demonstrates the effectiveness of our proposed method. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()